Spring Boot

Spring Boot is a framework built on Spring that simplifies application setup, configuration, and deployment.

It reduces boilerplate, provides sensible defaults, and accelerates production-ready development.

Spring Framework provides core features; Boot adds auto-configuration, starters, and operational tooling.

Automatic configuration of beans based on classpath, environment, and properties.

Predefined dependency bundles for specific capabilities (web, data, security, etc.).

spring-boot-starter-web.

@Configuration, @EnableAutoConfiguration, and @ComponentScan.

Automatic discovery of beans in package hierarchy from main app class.

It defines default scan scope for components/configuration.

Tool to generate Spring Boot project templates quickly.

Main app class, controllers/services/repositories, config, resources, tests.

application.properties or application.yml.

Yes, environment variables are part of property resolution.

Keeping config outside code for environment-specific deployment.

Environment-specific configuration activation (dev/test/prod, etc.).

spring.profiles.active via file/env/CLI.

Profile-specific property file loaded when that profile is active.

Boot can run with embedded Tomcat/Jetty/Undertow without external app server.

Tomcat (by default).

Run main method or package and execute jar.

Self-contained jar bundling app classes and dependencies.

Spring Boot launcher that sets up classpath and starts app.

Shortcut for REST controllers returning response bodies.

Maps HTTP requests to handler methods/classes.

Shortcut annotations for GET and POST HTTP methods.

Binds request payload to Java object.

Binds URI template variable to method parameter.

Binds query/form parameter to method parameter.

Typically Jackson auto-configured message converters.

Represents full HTTP response (status, headers, body).

Marks service-layer component for business logic.

Marks persistence component and enables exception translation patterns.

Injects dependencies managed by Spring container.

Constructor injection.

Immutability, explicit dependencies, test friendliness.

Injects single property value into fields/params.

Type-safe binding of grouped properties into POJOs.

Validation, structure, maintainability.

Production-ready endpoints for health, metrics, info, and management.

/actuator/health.

BOM-managed compatible dependency versions.

Increases conflict risk and maintenance burden.

Interface for logic execution after context startup.

Similar to CommandLineRunner with parsed application arguments.

Startup text/logo displayed on app launch.

Yes, via properties or programmatic config.

SLF4J facade with Logback backend (typical default).

Usually under src/main/resources/static.

Usually under src/main/resources/templates.

Development-time utilities (auto-restart, live reload conveniences).

No, development only.

App restarts when classpath changes in development.

Sensible defaults requiring minimal manual setup.

Yes, via explicit beans/properties/configuration.

Autowiring ambiguity; resolve with @Primary/@Qualifier.

Marks default bean candidate.

Selects a specific bean for injection.

Relying on magic defaults without understanding what got auto-configured.

Use condition evaluation report/logging/Actuator insights.

Start with defaults, then customize deliberately with clear rationale.

Conditional rules based on classpath, existing beans, properties, and environment.

@ConditionalOnClass, @ConditionalOnMissingBean, @ConditionalOnProperty.

User-defined bean prevents conflicting default bean creation.

Allows safe customization without disabling whole auto-config module.

Registers configuration properties classes for binding.

Use Bean Validation annotations with @Validated.

Flexible property naming formats mapping to same target fields.

Both supported; YAML is hierarchical and often cleaner for nested config.

Multi-document YAML with profile activation sections.

Built-in support for random values in config placeholders.

Dependency bundle simplifying testing with JUnit, assertions, mocks, etc.

Loads full application context for integration tests.

MVC slice test for controller layer only.

Slice test focused on JPA repositories.

Faster focused tests with reduced context size.

Replaces bean in test context with Mockito mock.

Convenient synchronous REST client for integration tests.

Starts embedded server on random port to avoid conflicts.

Test-specific bean configuration class.

Set test-specific properties via annotations/files/env.

Automatic schema/data script execution (config dependent).

Database migration tools integrated for versioned schema changes.

Repeatability, traceability, safer deployments.

Typically HikariCP.

Improves performance and controls DB resource usage.

Usually declarative via @Transactional.

Rollback on unchecked exceptions by default.

Keeps persistence context open during web request rendering (trade-offs exist).

Hidden queries in view layer and long-lived persistence context.

Configure which management endpoints are available and to whom.

Component adding subsystem-specific health checks.

Liveness: process alive; readiness: ready to serve traffic.

Metrics facade used by Boot to publish monitoring data.

Prometheus, Datadog, Graphite, Cloud monitoring systems.

Dimension-based filtering and dashboard slicing.

Correlate requests across services with trace/span IDs.

Include trace identifiers in logging context/output.

Global or endpoint-specific cross-origin request rules.

Centralized REST error mapping via @ControllerAdvice.

Standardized HTTP API error response structure.

Applies secure defaults when security starter is present.

Primary bean defining HTTP security rules.

Modern Boot/Security favors component-based SecurityFilterChain configuration.

Protection against forged state-changing requests in browser sessions.

Token-based auth (e.g., JWT), no server session state.

Authorization constraints on methods (e.g., @PreAuthorize).

Annotation-driven caching independent of provider implementation.

@Cacheable, @CachePut, @CacheEvict.

Periodic task execution via @Scheduled.

Background method execution via @Async and executors.

Control concurrency, queueing, and rejection behavior.

Coordinated shutdown allowing in-flight request handling.

Multiple property sources with deterministic override order.

Passing --key=value at startup to override config.

Adjust property sources early in application bootstrap.

Reusable internal dependency with auto-configuration conventions.

Standardize platform defaults across many services.

Diagnostic report explaining why auto-configurations matched or not.

Check classpath, properties, exclusions, and condition report.

Explicitly disabling specific auto-config classes.

Only with clear reason to prevent unintended behavior conflicts.

Choose based on workload, blocking dependencies, and team expertise.

Possible with caveats; usually choose one primary model per service.

Boot feature to import external config locations/sources.

Use vault/secret manager/env vars, never hardcode secrets.

Copy-pasting many properties without understanding defaults/impacts.

Prefer explicit critical config + observable runtime behavior.

Security fixes, performance improvements, ecosystem compatibility.

Conflicting transitive versions causing runtime/classpath issues.

Team can explain every major auto-config/customization in production.

Ahead-of-time processing generating optimized runtime setup metadata/code.

Improves startup and can reduce memory, especially for native deployments.

Compile app ahead-of-time to native binary (trade-offs in build/debug/dynamics).

Dynamic reflection needs explicit hints/registrations.

Declare reflection/resource/proxy needs for AOT/native compatibility.

Trim classpath, reduce bean count, lazy noncritical components, AOT where suitable.

Right-size caches, avoid unnecessary autoconfig modules, tune JVM/container limits.

Separate layers for dependencies/app classes to optimize Docker caching.

Faster incremental rebuilds and deployments.

Create OCI images without custom Dockerfile using buildpacks.

Service environments diverge unexpectedly over time.

Central config governance, validation, immutable release pipelines.

Avoid tight coupling and uncontrolled component scanning across modules.

Explicit package structure, architecture tests, limited exports.

Translates external models/protocols into internal domain language.

Write domain change and event record in one local transaction.

Improves reliability of event publication without distributed XA complexity.

Client-provided key preventing duplicate side effects on retries.

Network retries/timeouts can repeat requests unpredictably.

Circuit breakers, retries, rate limiting, bulkheads, time limiters.

Can amplify failures and overload dependencies.

Many clients retrying simultaneously during outages.

Exponential backoff, jitter, retry budgets, circuit breaking.

Separate resource pools per dependency/workload to limit blast radius.

Need to control producer/consumer demand to prevent overload.

Blocking calls can collapse non-blocking scalability.

Logs, metrics, traces, and health semantics built into service from start.

health, metrics, prometheus, info, env (carefully), threaddump (restricted).

They may expose sensitive internals and operational controls.

Instrument according to latency/error/availability objectives.

Tail latency reflects worst user experience and incident risk.

Misunderstanding proxy boundaries causing missing rollbacks/partial commits.

Stale data and inconsistent eviction under concurrent updates.

Define TTLs, explicit invalidation, key design, and fallback behavior.

Backward-compatible phased migrations aligned with rolling deploys.

Blue/green swaps environments; rolling replaces instances gradually.

Return partial responses/fallbacks when dependencies fail.

Refuse startup if critical dependencies/config are invalid.

Security-critical paths often fail-closed; noncritical observability may fail-open.

Minimize unused starters/libs to reduce attack surface and complexity.

Provide shared starters, baselines, templates, and governance for teams.

Recommended service scaffold with best-practice defaults.

Treating auto-configuration as magic and skipping architecture discipline.

Check security, observability, resilience, config validation, scaling behavior.

Measure throughput, latency percentiles, saturation, and dependency bottlenecks.

Validates resilience under dependency faults and partial outages.

Correlate traces, logs, metrics, recent deploy/config changes.

Keep Boot upgrades regular and customizations minimal/intentional.

They can explain framework behavior, not just copy configuration snippets.

Use Spring Boot for speed, but preserve explicit architecture and operational rigor.